Multispectral and panchromatic data fusion assessment without reference

This paper introduces a novel approach for evaluating the quality of pansharpened multispectral (MS) imagery without resorting to reference originals. Hence, evaluations are feasible at the highest spatial resolution of the panchromatic (PAN) sensor. Wang and Bovik’s image quality index (QI) provides a statistical similarity measurement between two monochrome images. The QI values between any couple of MS bands are calculated before and after fusion and used to define a measurement of spectral distortion. Analogously, QI values between each MS band and the PAN image are calculated before and after fusion to yield a measurement of spatial distortion. The rationale is that such QI values should be unchanged after fusion, i.e., when the spectral information is translated from the coarse scale of the MS data to the fine scale of the PAN image. Experimental results, carried out on very high-resolution Ikonos data and simulated Pleiades data, demonstrate that the results provided by the proposed approach are consistent and in trend with analysis performed on spatially degraded data. However, the proposed method requires no reference originals and is therefore usable in all practical cases.

[1]  S. Sides,et al.  Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic , 1991 .

[2]  Fionn Murtagh,et al.  Image restoration with noise suppression using the wavelet transform , 1994 .

[3]  L. Wald,et al.  Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images , 1997 .

[4]  J. Zhou,et al.  A wavelet transform method to merge Landsat TM and SPOT panchromatic data , 1998 .

[5]  Lucien Wald,et al.  Some terms of reference in data fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[6]  Xavier Otazu,et al.  Multiresolution-based image fusion with additive wavelet decomposition , 1999, IEEE Trans. Geosci. Remote. Sens..

[7]  L. Wald,et al.  Fusion of high spatial and spectral resolution images : The ARSIS concept and its implementation , 2000 .

[8]  Frank W. Gerlach,et al.  IKONOS technical performance assessment , 2001, SPIE Defense + Commercial Sensing.

[9]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[10]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

[11]  L. Alparone,et al.  GENERALISED LAPLACIAN PYRAMID-BASED FUSION OF MS + P IMAGE DATA WITH SPECTRAL DISTORTION MINIMISATION , 2002 .

[12]  Luciano Alparone,et al.  Image fusion—the ARSIS concept and some successful implementation schemes , 2003 .

[13]  Yun Zhang,et al.  Understanding image fusion , 2004 .

[14]  Luciano Alparone,et al.  A global quality measurement of pan-sharpened multispectral imagery , 2004, IEEE Geoscience and Remote Sensing Letters.

[15]  Te-Ming Tu,et al.  A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery , 2004, IEEE Geoscience and Remote Sensing Letters.

[16]  Guy Flouzat,et al.  Thematic and statistical evaluations of five panchromatic/multispectral fusion methods on simulated PLEIADES-HR images , 2005, Inf. Fusion.

[17]  Andrea Garzelli,et al.  PAN‐sharpening of very high resolution multispectral images using genetic algorithms , 2006 .

[18]  Klaus Steinnocher,et al.  Influence of image fusion approaches on classification accuracy: a case study , 2006 .

[19]  Lorenzo Bruzzone,et al.  Can multiresolution fusion techniques improve classification accuracy? , 2006, SPIE Remote Sensing.

[20]  Roger L. King,et al.  Estimation of the Number of Decomposition Levels for a Wavelet-Based Multiresolution Multisensor Image Fusion , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[21]  F. Nencini,et al.  Fusion of Panchromatic and Multispectral Images by Genetic Algorithms , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[22]  Luciano Alparone,et al.  MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery , 2006 .